Recently, learning data-driven behavioral priors has become a promising approach to solving long-horizon tasks. Given a large unlabeled offline dataset of robotic demonstrations solving a diverse set of tasks this family of approaches [1]}, [2]}, [3]} extract behavioral priors by fitting maximum likelihood expectation latent variable models to the offline dataset. The behavioral priors are then used to guide a Reinforcement Learning (RL) algorithm to solve downstream tasks. By selecting skills from the behavioral prior, the RL algorithm is able to explore in a structured manner and can solve long-horizon navigation and manipulation tasks. However, the generalization capabilities of RL with behavioral priors are limited since a different RL agent needs to be trained for each downstream task and training each RL agent often requires millions of environment interactions.
